Models are useful tools for understanding and predicting ecological patterns and processes. Under ongoing climate and biodiversity change, they can greatly facilitate decision‐making in conservation ...and restoration and help designing adequate management strategies for an uncertain future. Here, we review the use of spatially explicit models for decision support and to identify key gaps in current modelling in conservation and restoration. Of 650 reviewed publications, 217 publications had a clear management application and were included in our quantitative analyses. Overall, modelling studies were biased towards static models (79%), towards the species and population level (80%) and towards conservation (rather than restoration) applications (71%). Correlative niche models were the most widely used model type. Dynamic models as well as the gene‐to‐individual level and the community‐to‐ecosystem level were underrepresented, and explicit cost optimisation approaches were only used in 10% of the studies. We present a new model typology for selecting models for animal conservation and restoration, characterising model types according to organisational levels, biological processes of interest and desired management applications. This typology will help to more closely link models to management goals. Additionally, future efforts need to overcome important challenges related to data integration, model integration and decision‐making. We conclude with five key recommendations, suggesting that wider usage of spatially explicit models for decision support can be achieved by 1) developing a toolbox with multiple, easier‐to‐use methods, 2) improving calibration and validation of dynamic modelling approaches and 3) developing best‐practise guidelines for applying these models. Further, more robust decision‐making can be achieved by 4) combining multiple modelling approaches to assess uncertainty, and 5) placing models at the core of adaptive management. These efforts must be accompanied by long‐term funding for modelling and monitoring, and improved communication between research and practise to ensure optimal conservation and restoration outcomes.
Genetics of dispersal Saastamoinen, Marjo; Bocedi, Greta; Cote, Julien ...
Biological reviews of the Cambridge Philosophical Society,
February 2018, Letnik:
93, Številka:
1
Journal Article
Recenzirano
Odprti dostop
ABSTRACT
Dispersal is a process of central importance for the ecological and evolutionary dynamics of populations and communities, because of its diverse consequences for gene flow and demography. It ...is subject to evolutionary change, which begs the question, what is the genetic basis of this potentially complex trait? To address this question, we (i) review the empirical literature on the genetic basis of dispersal, (ii) explore how theoretical investigations of the evolution of dispersal have represented the genetics of dispersal, and (iii) discuss how the genetic basis of dispersal influences theoretical predictions of the evolution of dispersal and potential consequences.
Dispersal has a detectable genetic basis in many organisms, from bacteria to plants and animals. Generally, there is evidence for significant genetic variation for dispersal or dispersal‐related phenotypes or evidence for the micro‐evolution of dispersal in natural populations. Dispersal is typically the outcome of several interacting traits, and this complexity is reflected in its genetic architecture: while some genes of moderate to large effect can influence certain aspects of dispersal, dispersal traits are typically polygenic. Correlations among dispersal traits as well as between dispersal traits and other traits under selection are common, and the genetic basis of dispersal can be highly environment‐dependent.
By contrast, models have historically considered a highly simplified genetic architecture of dispersal. It is only recently that models have started to consider multiple loci influencing dispersal, as well as non‐additive effects such as dominance and epistasis, showing that the genetic basis of dispersal can influence evolutionary rates and outcomes, especially under non‐equilibrium conditions. For example, the number of loci controlling dispersal can influence projected rates of dispersal evolution during range shifts and corresponding demographic impacts. Incorporating more realism in the genetic architecture of dispersal is thus necessary to enable models to move beyond the purely theoretical towards making more useful predictions of evolutionary and ecological dynamics under current and future environmental conditions. To inform these advances, empirical studies need to answer outstanding questions concerning whether specific genes underlie dispersal variation, the genetic architecture of context‐dependent dispersal phenotypes and behaviours, and correlations among dispersal and other traits.
Dispersal is fundamental in determining biodiversity responses to rapid climate change, but recently acquired ecological and evolutionary knowledge is seldom accounted for in either predictive ...methods or conservation planning. We emphasise the accumulating evidence for direct and indirect impacts of climate change on dispersal. Additionally, evolutionary theory predicts increases in dispersal at expanding range margins, and this has been observed in a number of species. This multitude of ecological and evolutionary processes is likely to lead to complex responses of dispersal to climate change. As a result, improvement of models of species' range changes will require greater realism in the representation of dispersal. Placing dispersal at the heart of our thinking will facilitate development of conservation strategies that are resilient to climate change, including landscape management and assisted colonisation.
Process‐based models are becoming increasingly used tools for understanding how species are likely to respond to environmental changes and to potential management options. RangeShifter is one such ...modelling platform, which has been used to address a range of questions including identifying effective reintroduction strategies, understanding patterns of range expansion and assessing population viability of species across complex landscapes. Here we introduce a new version, RangeShifter 2.0, which incorporates important new functionality. It is now possible to simulate dynamics over user‐specified, temporally changing landscapes. Additionally, we integrated a new genetic module, notably introducing an explicit genetic modelling architecture, which allows for simulation of neutral and adaptive genetic processes. Furthermore, emigration, transfer and settlement traits can now all evolve, allowing for sophisticated simulation of the evolution of dispersal. We illustrate the potential application of RangeShifter 2.0's new functionality by two examples. The first illustrates the range expansion of a virtual species across a dynamically changing UK landscape. The second demonstrates how the software can be used to explore the concept of evolving connectivity in response to land‐use modification, by examining how movement rules come under selection over landscapes of different structure and composition. RangeShifter 2.0 is built using object‐oriented C++ providing computationally efficient simulation of complex individual‐based, eco‐evolutionary models. The code has been redeveloped to enable use across operating systems, including on high performance computing clusters, and the Windows graphical user interface has been enhanced. RangeShifter 2.0 will facilitate the development of in‐silico assessments of how species will respond to environmental changes and to potential management options for conserving or controlling them. By making the code available open source, we hope to inspire further collaborations and extensions by the ecological community.
In Focus: Li, X‐Y., & H. Kokko. (2021). Sexual dimorphism driven by intersexual resource competition: Why is it rare, and where to look for it? Journal of Animal Ecology, 00, 1–13. Ecological sexual ...dimorphism, that is differences between the sexes in traits that are naturally selected as opposed to sexually selected, is gaining increasing attention after having often been dismissed as the ‘less‐parsimonious’ explanation for differences between sexes. One potential driver of ecological sexual dimorphism is intersexual resource competition, in a process analogous to ecological character displacement between species; yet, clear empirical examples are scarce. Li and Kokko present mathematical models that introduce novel pieces to the puzzle: the role of the scale of mating competition and the spatial variation in resource availability. They show that ecological sexual dimorphism evolves when local mating groups are small (e.g. monogamous pairs) and when different resources are homogeneously available across habitats. Counterintuitively, larger mating groups (e.g. polygyny), and consequently higher intralocus sexual conflict, lead to sexual monomorphism. Habitat heterogeneity also leads to overlapping niches, although it can sometimes drive polymorphism within sexes. This study highlights why the conditions for intrasexual resource competition to drive evolution of sexual dimorphism are stringent, even in the absence of genetic constraints or competing species. Crucially, it highlights the importance of considering the mating system and the spatial scale of resource competition for understanding the occurrence of ecological sexual dimorphism, showing a large potential for future work considering different aspects of species’ life histories and spatial dynamics.
This In Focus article highlights the work of Li & Kokko (2021). Their models show that evolution of ecological sexual dimorphism requires narrow individual niche width, relative small scale of mating competition, low degrees of intralocus sexual conflict and reliable co‐presence of different resource types. They therefore provide predictions for where to look for evidence of ecological sexual dimorphism.
Reliably modelling the demographic and distributional responses of a species to environmental changes can be crucial for successful conservation and management planning. Process‐based models have the ...potential to achieve this goal, but so far they remain underused for predictions of species' distributions. Individual‐based models offer the additional capability to model inter‐individual variation and evolutionary dynamics and thus capture adaptive responses to environmental change.
We present RangeShiftR, an R implementation of a flexible individual‐based modelling platform which simulates eco‐evolutionary dynamics in a spatially explicit way. The package provides flexible and fast simulations by making the software RangeShifter available for the widely used statistical programming platform R. The package features additional auxiliary functions to support model specification and analysis of results. We provide an outline of the package's functionality, describe the underlying model structure with its main components and present a short example.
RangeShiftR offers substantial model complexity, especially for the demographic and dispersal processes. It comes with elaborate tutorials and comprehensive documentation to facilitate learning the software and provide help at all levels. As the core code is implemented in C++, the computations are fast. The complete source code is published under a public licence, making adaptations and contributions feasible.
The RangeShiftR package facilitates the application of individual‐based and mechanistic modelling to eco‐evolutionary questions by operating a flexible and powerful simulation model from R. It allows effortless interoperation with existing packages to create streamlined workflows that can include data preparation, integrated model specification and results analysis. Moreover, the implementation in R strengthens the potential for coupling RangeShiftR with other models.
Abstract
Time is running out to limit further devastating losses of biodiversity and nature's contributions to humans. Addressing this crisis requires accurate predictions about which species and ...ecosystems are most at risk to ensure efficient use of limited conservation and management resources. We review existing biodiversity projection models and discover problematic gaps. Current models usually cannot easily be reconfigured for other species or systems, omit key biological processes, and cannot accommodate feedbacks with Earth system dynamics. To fill these gaps, we envision an adaptable, accessible, and universal biodiversity modeling platform that can project essential biodiversity variables, explore the implications of divergent socioeconomic scenarios, and compare conservation and management strategies. We design a roadmap for implementing this vision and demonstrate that building this biodiversity forecasting platform is possible and practical.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Understanding and predicting the dynamics of range expansion is a major topic in ecology both for invasive species extending their ranges into non-native regions and for species shifting their ...natural distributions as a consequence of climate change. In an increasingly modified landscape, a key question is ‘how do populations spread across patchy landscapes?’ Dispersal is a central process in range expansion and while there is a considerable theory on how the shape of a dispersal kernel influences the rate of spread, we know much less about the relationships between emigration, movement and settlement rules, and invasion rates. Here, we use a simple, single species individual-based model that explicitly simulates animal dispersal to establish how density-dependent emigration and settlement rules interact with landscape characteristics to determine spread rates. We show that depending on the dispersal behaviour and on the risk of mortality in the matrix, increasing the number of patches does not necessarily maximise the spread rate. This is due to two effects: first, individuals dispersing at the expanding front are likely to exhibit lower net-displacement as they typically do not travel far before finding a patch; secondly, with increasing availability of high quality habitat, density-dependence in emigration and settlement can decrease the number of emigrants and their net-displacement. The rate of spread is ultimately determined by the balance between net travelled distance, the dispersal mortality and the number of dispersing individuals, which in turn depend on the interaction between the landscape and the species' dispersal behaviour. These results highlight that predicting spread rates in heterogeneous landscapes is a complex task and requires better understanding of the rules that individuals use in emigration, transfer and settlement decisions.
Understanding the dynamics of socio‐ecological systems is crucial to the development of environmentally sustainable practices. Models of social or ecological sub‐systems have greatly enhanced such ...understanding, but at the risk of obscuring important feedbacks and emergent effects. Integrated modelling approaches have the potential to address this shortcoming by explicitly representing linked socio‐ecological dynamics. We developed a socio‐ecological system model by coupling an existing agent‐based model of land‐use dynamics and an individual‐based model of demography and dispersal. A hypothetical case‐study was established to simulate the interaction of crops and their pollinators in a changing agricultural landscape, initialised from a spatially random distribution of natural assets. The bi‐directional coupled model predicted larger changes in crop yield and pollinator populations than a unidirectional uncoupled version. The spatial properties of the system also differed, the coupled version revealing the emergence of spatial land‐use clusters that neither supported nor required pollinators. These findings suggest that important dynamics may be missed by uncoupled modelling approaches, but that these can be captured through the combination of currently‐available, compatible model frameworks. Such model integrations are required to further fundamental understanding of socio‐ecological dynamics and thus improve management of socio‐ecological systems.
PDF
